Evaluating Scientific Visualization Using Cognitive Measures

In this position paper, we discuss the problems and advantages of using physiological measurements to to estimate cognitive load in order to evaluate scientific visualization methods. We will present various techniques and technologies designed to measure cognitive load and how they may be leveraged in the context of user evaluation studies for scientific visualization. We also discuss the challenges of experiments designed to use these physiological measurements.

[1]  Daniel Acevedo Feliz,et al.  Using Visual Design Experts in Critique-Based Evaluation of 2D Vector Visualization Methods , 2008, IEEE Transactions on Visualization and Computer Graphics.

[2]  Bhavin R. Sheth,et al.  Posterior Beta and Anterior Gamma Oscillations Predict Cognitive Insight , 2009, Journal of Cognitive Neuroscience.

[3]  Krista E. DeLeeuw,et al.  A Comparison of Three Measures of Cognitive Load: Evidence for Separable Measures of Intrinsic, Extraneous, and Germane Load , 2008 .

[4]  D. Leutner,et al.  Assessment of cognitive load in multimedia learning using dual-task methodology. , 2002, Experimental psychology.

[5]  Edward M. Bowden,et al.  Aha! Insight experience correlates with solution activation in the right hemisphere , 2003, Psychonomic bulletin & review.

[6]  Weidong Huang,et al.  Beyond time and error: a cognitive approach to the evaluation of graph drawings , 2008, BELIV '08.

[7]  J. Fermaglich.  Electric Fields of the Brain: The Neurophysics of EEG , 1982 .

[8]  Mustapha Mouloua,et al.  Assessing Mental Workload with Subjective Measures: An Analytical Review of the NASA-TLX Index Since its Inception , 1999 .

[9]  David H. Laidlaw,et al.  Thoughts on User Studies: Why, How and When , 1993 .

[10]  P. Chandler,et al.  Cognitive Load Theory and the Format of Instruction , 1991 .

[11]  Ben Shneiderman,et al.  Strategies for evaluating information visualization tools: multi-dimensional in-depth long-term case studies , 2006, BELIV '06.

[12]  Nadine B. Sarter,et al.  Pilot Interaction With Cockpit Automation II: An Experimental Study of Pilots’ Model and Awareness of the Flight Management System , 1994 .

[13]  Robert Michael Kirby,et al.  Comparing 2D vector field visualization methods: a user study , 2005, IEEE Transactions on Visualization and Computer Graphics.

[14]  V. Konovalov,et al.  Characteristics of the galvanic skin response and electrocardiogram in active and passive subjects under test conditions , 2006, Human Physiology.

[15]  Edward M. Bowden,et al.  The Prepared Mind , 2006, Psychological science.

[16]  Chris North,et al.  Toward measuring visualization insight , 2006, IEEE Computer Graphics and Applications.

[17]  R. Oliver.  Cognitive, affective, and attribute bases of the satisfaction response. , 1993 .

[18]  Edward M. Bowden,et al.  Neural Activity When People Solve Verbal Problems with Insight , 2004, PLoS biology.

[19]  Pat Hanrahan,et al.  Measuring the task-evoked pupillary response with a remote eye tracker , 2008, ETRA.

[20]  R. Likert.  “Technique for the Measurement of Attitudes, A” , 2022, The SAGE Encyclopedia of Research Design.

[21]  M. Sheelagh T. Carpendale,et al.  Evaluating Information Visualizations , 2008, Information Visualization.

[22]  E. Stuyven,et al.  The effect of cognitive load on saccadic eye movements. , 2000, Acta psychologica.

[23]  Daniel Acevedo Feliz,et al.  Subjective Quantification of Perceptual Interactions among some 2D Scientific Visualization Methods , 2006, IEEE Transactions on Visualization and Computer Graphics.

[24]  Michelle N. Lumicao,et al.  EEG correlates of task engagement and mental workload in vigilance, learning, and memory tasks. , 2007, Aviation, space, and environmental medicine.

[25]  Dieter Schmalstieg,et al.  Liver Surgery Planning Using Virtual Reality , 2006, IEEE Computer Graphics and Applications.

[26]  Chris Berka,et al.  Evaluation of an EEG workload model in an Aegis simulation environment , 2005, SPIE Defense + Commercial Sensing.